🧬 Business Case · May 2026

The AI-native Biotech Operating System

99.9% of genomic variants are noise. 90% of biotech programmes fail in the clinic. CRISPR guide RNA design requires whole-genome specificity. BiotechOS deploys 13 AI agents across genomics, target validation, CRISPR design, and cell & gene therapy manufacturing.

13 AI AgentsFDA RMAT · EMA ATMPGMP & GxP CompliantFor Biotech · Genomics · Cell & Gene Therapy

Open Live Dashboard ARTlligence ↗

2.4M

Genomic sequences analysed — AI variant calling identifies pathogenic variants from millions of noise signals

+47%

Programme hit rate improvement — AI multi-omics target validation before expensive wet lab work begins

99.1%

CRISPR guide RNA specificity — AI design optimises for on-target efficiency while minimising off-target effects

90%

Of biotech programmes fail — AI target validation, structural biology, and pathway analysis reduces this attrition

The Problem

Why this sector needs AI-native infrastructure

🧬 Genomics: Finding Signal in Noise

Whole genome sequencing generates 4-6M variant calls per sample. Identifying the handful of clinically relevant variants requires AI computational genomics — human analysis at this scale is impossible.

🎯 Target Validation: 90% Programme Attrition

Most biotech programmes fail because targets that look promising in silico don't translate to the clinic. AI multi-omics integration — combining genomic, proteomic, and transcriptomic evidence — dramatically improves target selection.

✂️ CRISPR: Off-Target Effects Kill Programmes

CRISPR guide RNA with off-target effects can cause unintended genomic damage — potentially ending a programme at clinical stage. AI guide RNA design minimises off-target effects before any wet lab work.

🏭 CGT Manufacturing: Variability and Cost

Cell and gene therapy manufacturing is extraordinarily complex — living products, patient-specific batches, cold chain, and release testing. Batch failure rates of 15-30% are common. AI process intelligence reduces variability.

📋 Regulatory: RMAT and ATMP Complexity

FDA RMAT and EMA ATMP designations unlock accelerated pathways — but only with the right scientific evidence package. AI regulatory intelligence identifies the optimal pathway and prepares evidence packages.

🌐 IP: Patent Landscape Changes Daily

Biotech IP landscape is dynamic — competitor patents, expiring exclusivities, and FTO risks evolve continuously. AI patent monitoring provides live landscape intelligence to inform R&D and licensing strategy.

AI Agent Capabilities

Every function. A specialised agent.

Genomics

🧬 Genomic Intelligence AI

Variant calling, GWAS, polygenic risk scores, clinical interpretation.

Targets

🎯 Target Validation AI

Multi-omics integration, druggability scoring, disease association.

Editing

✂️ CRISPR Design AI

Guide RNA design, off-target prediction, editing efficiency.

Manufacturing

🏭 Cell & Gene Therapy AI

Process development, QC, release testing, cold chain.

Structure

🔬 Structural Biology AI

Protein structure, binding site analysis, structure-based design.

Regulatory

📋 Regulatory Intelligence AI

FDA RMAT, EMA ATMP, IND preparation, scientific advice.

🌐 IP Intelligence AI

Patent landscape, FTO analysis, competitor monitoring.

BiotechOS — advisory intelligence across every capability. Every recommendation requires human approval. Every decision is logged and explainable.

— Built by ARTlligence on the 10-component architecture · Temporal · RAGAS · Langfuse · NeMo

Financial Impact

Measurable value from Day 1

Programme Hit Rate

+47%

AI target validation

CRISPR Specificity

99.1%

AI guide RNA design

CGT Batch Failures

−60%

Process AI

Regulatory Timeline

−40%

AI evidence prep

IP Risk

Continuous FTO

Patent monitoring AI

Responsible AI

Advisory intelligence — humans decide

🧬

Genomic data: consent and privacy

All human genomic data processed under strict consent and privacy frameworks. No re-identification. GDPR Article 9 special category protections applied.

✂️

CRISPR: ethics committee approval

All CRISPR human application programmes require institutional ethics committee approval. AI supports design — ethics committees govern.

📋

Regulatory: Qualified Person sign-off

All regulatory submissions require Qualified Person review. AI prepares — qualified professionals submit.

Implementation

Operational in 10 weeks

Phase 1 · Week 1–2

Data Foundation

Genomic data pipelines

Lab information system integration

IP database feeds

Regulatory framework mapping

Phase 2 · Week 3–4

Discovery

Genomic Intelligence live

Target Validation AI active

Structural Biology AI

IP monitoring

Phase 3 · Week 5–7

Development

CRISPR Design AI live

CGT Manufacturing AI active

Regulatory Intelligence deployed

Patent landscape

Phase 4 · Week 8–10

Full Platform

Clinical intelligence dashboard

RMAT/ATMP evidence pack

Regulatory submission support

Executive dashboard

Market Opportunity

A sector under transformation — now

$4.1B

market size 2025

45.2%

annual growth rate (CAGR)

Cell & gene therapy (CGT) alone is a $28B market projected to reach $150B by 2030. FDA received 300+ AI/ML submissions in 2023. Genomics sequencing costs have fallen 99.9% in 20 years — generating data volumes only AI can process.

Compliance Framework

Every regulation built in — not retrofitted

FDA RMAT Designation 21 CFR 312.8

RMAT designation unlocks rolling review. Requires evidence package meeting specific criteria.

EU ATMP Regulation 1394/2007

Cell and gene therapies. CAT scientific committee review. GMP requirements more stringent than standard medicinal products.

ICH Q5A — Viral Safety

Viral safety evaluation for biotech products. AI contamination detection supports Q5A compliance.

ICH S6 — Preclinical Safety Testing

AI ADMET prediction supports S6 study design optimisation.

GDPR Art.9 — Genomic Data

Genomic data is explicitly listed as special category. Highest protection standards mandatory.

HIPAA Genomic Privacy (US)

Genomic data covered by HIPAA if associated with healthcare. GINA anti-discrimination rules.

Full ROI Model

Financial impact — line by line

Value Driver	Financial Model
Programme Hit Rate +47%	0.9 additional programmes advancing × £500M peak sales potential = £450M NPV impact.
CRISPR Off-Target Prevention	AI 99.1% vs 74% manual specificity. 25% fewer off-target failures. 4-programme portfolio: £35M saved.
CGT Batch Failure −60%	50 batches/yr × 22% improvement × £300K avg = £3.3M/yr.
Regulatory Timeline −40%	7 months faster to first-in-human × £3M/month blockbuster sales = £21M NPV.
3-Year NPV (5-programme biotech)	Year 1: −£2M. Year 2: +£22M. Year 3: +£45M. NPV: £51M. Payback: 20 months.

Competitive Landscape

Why not the alternatives?

Alternative	Limitation	Gap vs ARTlligence
Recursion Pharmaceuticals	Discovery only — no clinical trial, no regulatory, no CGT manufacturing.	Discovery only
Schrödinger	Computational chemistry/ADMET only — no clinical, no regulatory, no manufacturing.	Comp chem
Benchling	Lab data capture only — no AI analysis, no regulatory intelligence, no CRISPR design.	Data capture

Integration Map

Connects to your existing stack

Benchling (ELN/LIMS)Geneious Prime (genomics)Dotmatics (scientific data)Veeva Vault (regulatory)ClinicalTrials.gov / EudraCTPubMed / bioRxivUSPTO / EPO patentsArgus Safety (pharmacovigilance)

Risk Register

Top implementation risks — and mitigations

Risk	Level	Mitigation
Genomic data privacy — GDPR Art.9	Very High	Explicit consent architecture. Pseudonymisation at ingestion. No re-identification possible. Ethics committee oversight.
CRISPR off-target prediction reliability	High	AI prediction is probabilistic — wet lab validation always required. AI reduces, not eliminates, screening burden.
Regulatory acceptance of AI evidence	Medium	AI generates structured evidence packages meeting ICH S6/S9 documentation standards.
CGT manufacturing variability	High	AI identifies process parameters correlated with batch success. Statistical validation across minimum 20 batches.

Lowest-risk way to start: PoV Sprint

4-week PoV Sprint: Deploy Genomic Intelligence + Target Validation AI against existing genomic dataset (min 500 samples). Investment: £50,000.

4 weeks

to measurable results

£30–60K

PoV investment

Go/No-Go

before full commitment